Task Scheduling Mechanism Using Multi-criteria Decision-making Technique, MACBETH in Cloud Computing

  • Suvendu Chandan Nayak
  • Sasmita Parida
  • Chitaranjan Tripathy
  • Prasant Kumar Pattnaik
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 710)

Abstract

Cloud computing is the new era of Internet technology which provides various utilities and computing resources from the pool of resources on the basis of “pay per Use”. It is challenging one to allocate required on-demand resources for all the users’ request. Meanwhile, the service provider aims toward a better resource utilization. These user requests are called task, if task execution is bounded by time limit which is called deadline-based task. The deadline-based tasks have different parameters. To schedule these tasks, researchers proposed many works based upon these parameters. However, in this work, we considered the scheduling of deadline-based task that is a Multi-criteria Decision-making problem due to different task’s parameters associated with it. The work is proposed to implement Measuring Attractiveness through a Category-Based Evaluation Technique (MACBETH) to ranking the deadline-based task by which many tasks can meet their deadline. The results of the proposed work are quite good as compared to the existing mechanisms.

Keywords

Cloud computing Task scheduling Resource utilization Deadline-based task MCDM MACBETH 

References

  1. 1.
    B. Sotomayor, R. S. Montero, I. M. Llorente, and I. Foster, “Virtual infrastructure management in private and hybrid clouds,” IEEE Internet Comput., vol. 13, pp. 14–22, 2009.Google Scholar
  2. 2.
    O. Project, “OpenNebula 4.4 Design and Installation Guide,” 2014.Google Scholar
  3. 3.
    A. Nathani, S. Chaudhary, and G. Somani, “Policy based resource allocation in IaaS cloud,” Futur. Gener. Comput. Syst., vol. 28, no. 1, pp. 94–103, 2012.Google Scholar
  4. 4.
    D. G. Feitelson, “Experimental Analysis of the Root Causes of Performance Evaluation Results : A Backfilling Case Study,” pp. 1–12.Google Scholar
  5. 5.
    S. C. Nayak and C. Tripathy, “Deadline sensitive lease scheduling in cloud computing environment using AHP,” J. King Saud Univ. - Comput. Inf. Sci., 2016.Google Scholar
  6. 6.
    C. A. BANA E COSTA, J.-M. DE CORTE, and J.-C. VANSNICK, “Macbeth,” Int. J. Inf. Technol. Decis. Mak., vol. 11, no. 2, pp. 359–387, 2012.Google Scholar
  7. 7.
    B. Sotomayor, R. S. Montero, I. M. Llorente, and I. Foster, “Resource Leasing and the Art of Suspending Virtual Machines,” 2009 11th IEEE Int. Conf. High Perform. Comput. Commun., vol. 0, pp. 59–68, 2009.Google Scholar
  8. 8.
    I. Foster, “Virtual infrastrueture Manageent in Private and Hybrid Clouds,” IEEE Internet Comput., 2009.Google Scholar
  9. 9.
    D. G. Feitelson, “Utilization and Predictability in Scheduling the IBM SP2 with Back lling 1 Introduction,” Science (80-.).Google Scholar
  10. 10.
    E.-K. Byun, Y.-S. Kee, J.-S. Kim, and S. Maeng, “Cost optimized provisioning of elastic resources for application workflows,” Futur. Gener. Comput. Syst., vol. 27, no. 8, pp. 1011–1026, 2011.Google Scholar
  11. 11.
    X. Li and Z. Cai, “Elastic Resource Provisioning for Cloud Workflow Applications,” IEEE Trans. Autom. Sci. Eng., no. January, pp. 1–16, 2015.Google Scholar
  12. 12.
    F. Dong and S. G. Akl, “Scheduling Algorithms for Grid Computing: State of the Art and Open Problems,” pp. 1–55, 2006.Google Scholar
  13. 13.
    A. S. Survey, “VIKOR and its Applications :,” vol. 5, no. June, pp. 56–83, 2014.Google Scholar
  14. 14.
    Q. Z. D. L. Y. Yang, “VIKOR Method with Enhanced Accuracy for Multiple Criteria Decision Making in Healthcare Management,” 2013.Google Scholar
  15. 15.
    A. Gani, N. B. Anuar, M. Shiraz, M. N. Haque, and I. T. Haque, “Cloud Service Selection Using Multicriteria Decision Analysis,” vol. 2014, 2014.Google Scholar
  16. 16.
    C. Markou, G. K. Koulinas, and A. P. Vavatsikos, “Project Resources Scheduling and Leveling Using Multi-Attribute Decision Models: Models Implementation and Case Study,” Expert Syst. Appl., vol. 77, pp. 160–169, 2017.Google Scholar
  17. 17.
    I. Solis Moreno, P. Garraghan, P. Townend, and J. Xu, “Analysis, Modeling and Simulation of Workload Patterns in a Large-Scale Utility Cloud,” IEEE Trans. Cloud Comput., vol. PP, no. c, pp. 1–1, 2014.Google Scholar
  18. 18.
    H. Arabnejad, J. G. Barbosa, and R. Prodan, “Low-time complexity budget-deadline constrained workflow scheduling on heterogeneous resources,” Futur. Gener. Comput. Syst., vol. 55, pp. 29–40, 2016.Google Scholar
  19. 19.
    S. Parida, S. C. Nayak, and C. Tripathy, “Truthful Resource Allocation Detection Mechanism for Cloud Computing,” in WCI, 2015, pp. 487–491.Google Scholar
  20. 20.
    S. C. Nayak, S. Parida, C. Tripathy, P. K. Pattnaik, “Resource allocation policies in cloud computing environment,” Advancing Cloud Database Systems and Capacity Planning With Dynamic Applications, 2017.Google Scholar
  21. 21.
    S. Parida, S. C. Nayak, P. Priyadarshi, P. K. Pattnaik, G. Ray, “Petri net: Design and analysis of parallel task scheduling algorithm,” Lecture Notes in Electrical Engineering, vol-443, 2018.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Suvendu Chandan Nayak
    • 1
    • 2
  • Sasmita Parida
    • 1
  • Chitaranjan Tripathy
    • 2
  • Prasant Kumar Pattnaik
    • 3
  1. 1.Department of Computer Science and EngineeringC V Raman College of EngineeringBhubaneswarIndia
  2. 2.Veera Surendra Sai University of TechnologyBurlaIndia
  3. 3.School of Computer EngineeringKIIT UniversityBhubaneswarIndia

Personalised recommendations